Successful retailers know that tracking and analyzing foot traffic reveals critical insights about customer behavior. When you understand visitor patterns, you can create intuitive shopping environments, deliver exceptional customer experiences, and significantly increase sales.
The market shows positive momentum, with retail foot traffic growing 0.4% year-over-year in 2024, despite some monthly variations caused by weather disruptions and calendar shifts.
But here's the challenge many retailers face: A customer browses your products online, adds items to their cart, but doesn't complete the purchase. Three days later, they walk into your physical store. Without unified commerce analytics that connect online browsing with in-store foot traffic, this valuable customer journey remains invisible—and a significant sales opportunity might be missed.
This is why modern retailers are moving beyond basic foot traffic counting to implement comprehensive analytics that connect online and offline customer behavior. Ahead, you'll learn how to collect and leverage foot traffic data to create a unified commerce experience that drives revenue for your retail business.
What is retail foot traffic?
Retail foot traffic, or footfall data, measures how many people enter a physical store during a specific time frame. Store owners use visitor movement data to understand when customers visit, where they go inside the store, and how visits relate to actual sales.
The main reason for collecting foot traffic data information is to count visitors at key locations within the store. These important spots (called "points of interest", or POI for short) include store entrances, product displays, department counters, and temporary sales areas.
Tracking foot traffic data offers businesses a range of valuable customer and business insights, including:
- The number of people who visit over a defined time period
- The busiest days and times for business
- The average duration of each visitor’s stay
- How many people walk past the store rather than enter it
- The most frequently visited parts of the store
- The effectiveness of marketing and sales campaigns
6 ways to collect retail foot traffic data
Reliable data collection can be achieved through a combination of manual or automated methods, including:
1. Mobile devices
When people visit stores with their phones, they might share location data if they use the store's app and allow location sharing. This happens through GPS mobile location data or small devices called beacons.
Pros:
- Provides detailed customer journey mapping within stores
- Offers real-time data collection without additional hardware
- Enables personalized marketing based on location
- Can integrate with other retail systems for comprehensive analytics
Cons:
- Many shoppers opt out of location tracking, creating sampling bias
- Privacy concerns may affect customer perception
- Requires complementary methods to ensure complete customer data collection
2. People counting sensors
People counting sensors measure visitor traffic at specific locations using light beams or thermal detection. Retailers place these sensors at strategic points like entrances or promotional areas to track foot traffic patterns.
Solutions like Dor People Counter use thermal sensing to monitor foot traffic and integrate seamlessly with Shopify POS. Unlike competitors' systems that require complex middleware to connect with your commerce platform, Dor's integration with Shopify provides a unified data view that lets you compare foot traffic alongside sales, inventory, and customer data—all within one platform.
Pros:
- High accuracy at specific choke points
- Relatively low maintenance requirements
- Privacy-friendly as sensors don't collect personal data
- Easy installation and battery-powered options available
Cons:
- Limited to counting at fixed locations only
- Cannot track movement throughout the entire store
- Requires multiple sensors for comprehensive coverage
- Sensors may have difficulty distinguishing between individuals in groups
3. Wi-Fi traffic
Use Wi-Fi networks to collect foot traffic data when shoppers connect their devices. Foot traffic data providers like Aislelabs use Wi-Fi systems to generate heat maps, count visitors, and deliver targeted marketing campaigns in-store. It can even identify new versus returning customers based on their device’s internet connection.
Pros:
- Creates detailed heat maps of store activity
- Distinguishes between new and returning visitors
- Requires minimal additional infrastructure if Wi-Fi already exists
Cons:
- Only captures data from visitors who connect to the network
- Signal interference can affect accuracy
- May require technical expertise to implement and maintain the system
4. Video analytics platforms
Video analytics platforms use security cameras (CCTV) to collect and analyze visitor information in stores. Retailers install special cameras that connect to analysis software. These systems can spot patterns in how people move around, which areas get busy, and even detect suspicious activity.
Pros:
- Provides comprehensive visual data to understand customer behavior
- Offers dual functionality with security systems
- Can detect anomalies and security concerns simultaneously
Cons:
- Higher initial investment and ongoing costs
- Requires significant processing power and storage
- More complex implementation than other solutions
- May raise privacy concerns among shoppers
5. Manual counting
Counting visitors by hand using click counters is an old-fashioned but affordable way to track store traffic. This method works well for collecting focused information during important sales periods without buying expensive technology.
Pros:
- No technology investment required
- Can be implemented immediately
- Easy to train staff to use
- Flexible deployment during specific time periods
Cons:
- Labor-intensive and pulls staff from other duties
- Prone to human error and inconsistency
- Difficult to maintain during busy periods
- Limited data insights compared to automated systems
6. POS data
Shopify POS helps store owners manage every aspect of their omnichannel retail business in a single commerce operating system. This system includes the card reader and barcode scanner you use to ring up sales, plus software that counts how many people visit your store.
The biggest advantage to using Shopify POS data is that you can compare how many browsers become buyers. Comparing the number of visitors to actual sales recorded in the system.
Pros:
- Shows the direct link between store visits and actual purchases
- Tells you exactly how many visitors end up buying something
- Helps you see which products sell best at different store locations
- Makes it easier to schedule staff during your busiest times
- Works with other Shopify tools to give you a complete view of your business
Cons:
- Only tracks people who buy something, not those who just browse
- Needs to be connected with other counting methods to see all visitors
- Staff need training to use the POS system correctly—though many retailers report a significantly lower investment with Shopify
What can foot traffic data tell you?
Retail foot traffic data reveals how customers move through your store, when they shop, and what influences their buying decisions.
The right tracking system can help uncover a ton of useful insights, like:
- Peak days and hours: See exactly when your store is busiest throughout the day, week, and year. This lets you schedule staff rotas efficiently, plan restocking, and prepare for rush periods.
- Customer movement patterns: Use foot traffic data to learn how shoppers navigate your store. Do they turn left when entering? Which aisles do they visit most? Which sections do they skip? This information helps you arrange your store’s layout and place products where customers are most likely to see them.
- Dwell time: Discover which areas of your store hold customers' attention longest. Areas with high dwell time but low sales might need better signage or product arrangements to convert interest into purchases.
- Conversion rates: Compare visitor counts to actual sales to understand what percentage of browsers become buyers. Low conversion in high-traffic areas signals potential issues with pricing, product selection, or customer service.
- Outside influences: Identify how external factors affect your traffic. Do more people visit when it's raining? Does a nearby event boost your numbers? Understanding these patterns helps you prepare for predictable traffic fluctuations.
- Customer demographic data: Learn who shops at your store by analyzing when different customer segments visit. For example, parents with young children might shop during different hours than college students or business professionals.
Bridging the online-offline divide with unified commerce
While traditional retailers still treat e-commerce and in-store traffic as separate data silos, forward-thinking brands are embracing unified commerce to gain a complete view of the customer journey.
Unified commerce goes beyond omnichannel by connecting all customer touchpoints through a single commerce operating system. This approach allows retailers to:
- Recognize when an online browser becomes an in-store buyer
- Understand how digital marketing impacts physical store visits
- Create personalized experiences based on a customer's complete shopping history
- Make inventory decisions based on holistic demand patterns across channels
The retailers who are winning today don't just count foot traffic—they connect it to their entire commerce ecosystem. When you can see how your online efforts drive in-store visits and vice versa, you unlock growth opportunities that siloed systems simply can't identify.
This is why Shopify has built its platform as a unified commerce solution from the ground up, rather than connecting separate systems with costly middleware like many competitors.
Uses cases of foot traffic analytics
Combined with other data points like daily sales, total revenue, and conversion rates, retail foot traffic can help you effectively plan better customer experiences, introduce experiential retail, and inform sales and marketing campaigns to drive more visitors and business.
The most common use cases for retail foot traffic data include:
Optimize store layouts
Foot traffic analytics let you analyze where customers go in the store and which areas they avoid. This lets you identify inefficiencies in your store layout, and experiment with different arrangements and product placements to improve traffic flow.
For example, if data shows customers rarely visit your back corner display, you might move popular items to that area to draw traffic deeper into the store. Or if most people turn right when entering (a common shopping behavior), you can place high-margin products in this high-visibility zone.
Build effective marketing, sales, and merchandising strategies
Foot traffic data helps measure the real-world impact of your marketing efforts. When you launch a new ad campaign or sales promotion, foot traffic tracking systems show whether more people actually visit your store as a result.
You can also test different in-store promotions by tracking traffic to specific display areas. If a new endcap display isn't attracting attention despite being well-stocked with sale items, you might need better signage or a different location within the store.
Ensure adequate staffing
Nothing frustrates customers more than waiting for help because a store is understaffed. Foot traffic data shows precisely when your busy periods occur—not just by day, but by hour.
Retailers can discover surprising patterns, like unexpected rush periods on weekday afternoons or slower-than-expected weekend mornings. With accurate foot traffic analytics, you can schedule more staff during peak hours and reduce staffing during consistently slow periods.
💡Tip: Use the Easyteam app to manage your staff in Shopify POS. The team management tool can arrange staff rotas, track commission, and manage store checklists within the POS interface your team already knows and loves.
Plan inventory
Foot traffic patterns help predict which products will sell fastest and when. You can anticipate demand and order inventory by tracking which departments or displays attract the most visitors.
For seasonal businesses, historical foot traffic data is particularly valuable. It shows when customer interest starts to build for holiday merchandise or seasonal items, helping you time your inventory orders perfectly.
Plus, foot traffic analytics help identify opportunities for cross-merchandising. If your data shows most customers visit the accessory department and the shoe section, displaying complementary items together might increase basket size.
Identify growth opportunities
Before opening additional locations, retailers perfect their operations at existing stores using foot traffic insights. Analyzing which store layouts, staffing models, and product mixes attract the most customers (and drive the most sales), you can create a proven formula for success.
For multi-location retailers, comparing foot traffic patterns across stores can reveal why some locations outperform others. Perhaps your highest-performing store has a layout that naturally encourages shoppers to explore more departments—a strategy you can implement across all locations to achieve similar results.
How to analyze retail foot traffic data
1. Establish data collection procedures
Set up a reliable and consistent data collection process that defines how you’ll collect foot traffic data.
First, decide how frequently you’ll pull data, like daily, weekly, or monthly. The cadence depends on the nature of your store traffic (e.g., high volume vs. moderate) and your operational needs (e.g., restocking schedules, marketing campaigns).
Implement dedicated tracking technology like Dor to track store visitors in near real-time. Then, use Shopify’s unified data model to consolidate transactional data, orders, and inventory details from your sales channels. This helps you see how in-store visitors correlate with actual purchases.
2. Identify key metrics
Analyzing foot traffic data isn’t as simple as counting how many people enter your store. Once your data collection infrastructure is in place, track these retail metrics to get the big picture on in-store customer behavior:
- Foot traffic volume: The total number of store visitors within a specific timeframe.
- Dwell time: How long customers spend in your store on average—longer times typically indicate higher engagement levels.
- Conversion rate: The percentage of visitors who purchase during their visit.
- Average basket size: The average amount each customer spends per transaction.
- Return visits: The frequency customers revisit your store.
- Inventory turnover: How quickly products sell through.
💡Tip: Shopify’s unified data model pulls customer, order, and inventory data from all sales channels into one business “brain”. This approach delivers 22% better total cost of ownership, with 6% lower one-time costs and up to 21% lower training and onboarding costs.
“Being able to unify our tech stack and lower its footprint has changed what we can do as a company,” says Ariel Kaye, founder of Parachute. “This creates more focus for us and allows us to spend our resources building differentiating features instead of basic functionality, while reducing risk.”
3. Analyze foot traffic trends
With metrics in hand, run a data analysis to identify meaningful patterns. For example, does the weather impact how likely customers are to visit your store? Does including a promotion in your window display entice passersby to come in? Which social media channels drive the most foot traffic?
You can also break down foot traffic by time of day to optimize staffing schedules, plan restocking during quieter periods, and schedule events when traffic is historically lower.
4. Create heat maps
Heat maps show where customers go in your store. They show which displays customers notice most, which store areas they ignore, and where they get stuck. Red areas show where many customers spend time, while blue areas get less attention.
With this information, store management can move popular products to quiet areas to spread traffic more evenly, or place impulse-buy items where people naturally pause.
5. Use predictive analytics
Don’t just take your data at face value. Leverage historical information to predict future patterns in foot traffic, conversions, and seasonal peaks.
Retail predictive insights incorporates multiple variables like promotions, weather, and local events for more accurate predictions. For example, a new store with limited data might not know which promotions are most likely to drive customers in-store. It could merge foot traffic data with market trends and wider customer behavior patterns using machine learning algorithms to inch closer to the answer.
Security concerns with foot traffic data
Retail foot traffic data falls under the category of consumer information. That means you must be cautious about how you collect data and how you use it to inform your retail strategies. You need to ensure that you adhere to all local and federal privacy laws when collecting and storing retail foot traffic data.
Some important considerations for retailers:
- Privacy regulations like the European Union's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) govern how this personal data can be collected, stored, and used.
- Combined data from multiple sources can create detailed shopper profiles with personally identifiable information, which is subject to stricter privacy protections.
- Retailers should be transparent about foot traffic data collection practices and provide customers with clear opt-in or opt-out choices.
- Implementing robust data security measures is essential to protect sensitive information.
When in doubt, review applicable regulations and consult with privacy law experts to ensure full compliance.
Use foot traffic data to make smarter retail decisions
Every footstep in your store is an opportunity. Don’t let it go to waste.
Foot traffic data helps retailers track the number of visitors to specific points of interest (POI), or high priority locations, in a retail store, within a defined period of time. This data uncovers trends and foot traffic patterns, telling you which parts of the store get the highest traffic and which products are shown the most interest—key insight you can use to improve the retail experience.
The best part? Tracking foot traffic is not complicated with Shopify POS. The system integrates with sensors like Dor to easily see how consumers’ in-store shopping habits impact sales—and every piece of data you collect is unified in one place.
Retail foot traffic FAQ
How do you track foot traffic in a store?
Retail stores track foot traffic using dedicated sensors like thermal counters, beam breaks, or camera systems installed at entrances and key areas. Many retailers also leverage their WiFi networks to detect customer devices or use POS integration to correlate visitor counts with transaction data.
How do you find footfall data?
Footfall data is collected through dedicated tracking systems that store information in cloud-based dashboards or integrated retail analytics platforms. Historical footfall information can be accessed through your traffic counter's reporting interface or exported to other business intelligence tools for deeper analysis.
Is foot traffic a KPI?
Yes, foot traffic is a fundamental key performance indicator (KPI) for retail businesses as it measures store popularity and the effectiveness of external marketing efforts. It serves as the essential denominator for calculating other critical metrics like conversion rate and provides insight into overall store performance.
How do you record foot traffic?
Methods to record foot traffic include:
- Automated counting systems
- Mobile devices
- Wi-Fi traffic
- Manual counting
- POS data
